Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/57874
Título: | Deep learning based pipeline for fingerprinting using brain functional MRI connectivity data |
Autor(es): | Lori, Nicolás F. Ramalhosa, Ivo Marques, Paulo César Gonçalves Alves, Victor |
Palavras-chave: | Deep-Learning fMRI Fingerprinting Data-processing Pipeline |
Data: | 2018 |
Editora: | Elsevier 1 |
Revista: | Procedia Computer Science |
Resumo(s): | In this work we describe an appropriate pipeline for using deep-learning as a form of improving the brain functional connectivity-based fingerprinting process which is based in functional Magnetic Resonance Imaging (fMRI) data-processing results. This pipeline approach is mostly intended for neuroscientists, biomedical engineers, and physicists that are looking for an easy form of using fMRI-based Deep-Learning in identifying people, drastic brain alterations in those same people, and/or pathologic consequences to people’s brains. Computer scientists and engineers can also gain by noticing the data-processing improvements obtained by using the here-proposed pipeline. With our best approach, we obtained an average accuracy of 0.3132 ± 0.0129 and an average validation cost of 3.1422 ± 0.0668, which clearly outperformed the published Pearson correlation approach performance with a 50 Nodes parcellation which had an accuracy of 0.237. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/57874 |
DOI: | 10.1016/j.procs.2018.10.129 |
ISSN: | 1877-0509 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Lori 2018 vCT.pdf | 667,52 kB | Adobe PDF | Ver/Abrir |